ADR-0030: Software and OS Update Strategy

Status

Proposed

Context

As the Qubinode Navigator project evolves, it must handle continuous updates across multiple dimensions:

Update Challenges

New Operating Systems: RHEL 11, Ubuntu LTS versions, new CentOS Stream releases
Software Version Updates: Ansible core updates, Python version changes, container runtime updates
Collection Dependencies: Updates to qubinode_kvmhost_setup_collection and other Ansible Galaxy collections
Security Patches: Critical vulnerabilities requiring immediate updates across the stack
Plugin Ecosystem: New plugins, plugin updates, and plugin compatibility management
AI Model Updates: New language models, improved inference engines, updated training data

Current Limitations

Manual Update Process: No systematic approach for handling new OS or software versions
Fragmented Testing: Updates tested in isolation without comprehensive integration validation
Documentation Lag: Documentation updates lag behind software changes
Compatibility Risks: New versions may break existing deployments without proper validation

The plugin framework (ADR-0028) and AI assistant (ADR-0027) provide opportunities for automated update detection, validation, and deployment.

Decision

Implement a comprehensive, automated update strategy that leverages the plugin architecture and AI assistant to handle software and OS updates systematically:

1. Automated Update Detection System

class UpdateDetector:
    def detect_updates(self) -> List[UpdateCandidate]:
        """Detect available updates across all components"""
        return [
            self.check_os_updates(),           # New OS versions
            self.check_software_updates(),     # Ansible, Python, etc.
            self.check_collection_updates(),   # Galaxy collections
            self.check_plugin_updates(),       # Plugin ecosystem
            self.check_security_updates(),     # CVE patches
            self.check_ai_model_updates()      # AI models and training data
        ]

2. Compatibility Matrix Management

# compatibility-matrix.yml
compatibility:
  rhel:
    "10": 
      python: ["3.12", "3.13"]
      ansible_core: ["2.18.1+"]
      collections:
        qubinode_kvmhost_setup: ">=2.1.0"
    "11":
      python: ["3.13", "3.14"]
      ansible_core: ["2.20.0+"]
      status: "testing"
      
  plugins:
    ai_assistant:
      compatible_models: ["granite-4.0-micro", "granite-5.0-micro"]
      min_memory: "4GB"
      
  collections:
    qubinode_kvmhost_setup:
      "2.1.0":
        os_support: ["rhel-9", "rhel-10", "rocky-9"]
        breaking_changes: ["dnf_modularity_removed"]

3. Staged Update Pipeline

Update Pipeline Stages:
Detection → Automated scanning for new versions
Compatibility → Check against compatibility matrix  
Testing → Automated testing in isolated environments
Validation → Integration testing across OS matrix
Staging → Deploy to staging environment
Production → Controlled rollout with rollback capability

4. Plugin-Based Update Management

class UpdatePlugin(QubiNodePlugin):
    def detect_os_updates(self) -> List[OSUpdate]:
        """Detect new OS versions and compatibility"""
        pass
        
    def validate_compatibility(self, update: Update) -> ValidationResult:
        """Check update against compatibility matrix"""
        pass
        
    def create_test_environment(self, update: Update) -> TestEnvironment:
        """Create isolated test environment for validation"""
        pass
        
    def execute_update(self, update: Update) -> UpdateResult:
        """Execute update with rollback capability"""
        pass

5. AI-Assisted Update Management

class AIUpdateAssistant:
    def analyze_update_impact(self, update: Update) -> ImpactAnalysis:
        """AI analysis of update impact and risks"""
        pass
        
    def generate_update_plan(self, updates: List[Update]) -> UpdatePlan:
        """Generate optimal update sequence and timing"""
        pass
        
    def provide_update_guidance(self, user_context: Context) -> Guidance:
        """Interactive guidance for update decisions"""
        pass

Consequences

Positive

Proactive Updates: Automatic detection and notification of available updates
Risk Mitigation: Comprehensive testing before production deployment
Compatibility Assurance: Matrix-based validation prevents breaking changes
Automated Testing: Reduces manual effort and human error in update validation
AI Guidance: Intelligent recommendations for update timing and sequencing
Rollback Safety: Automated rollback capability for failed updates
Documentation Sync: Automatic documentation updates with software changes

Negative

Infrastructure Complexity: Requires sophisticated testing and staging infrastructure
Initial Setup Effort: Significant work to establish compatibility matrices and test automation
Resource Requirements: Additional compute resources for testing environments
Maintenance Overhead: Compatibility matrix and test suites require ongoing maintenance

Risks

False Positives: Automated detection may flag non-critical updates as urgent
Test Environment Drift: Test environments may not perfectly match production
Update Conflicts: Multiple simultaneous updates may have unexpected interactions
AI Accuracy: AI recommendations may be incorrect for edge cases

Implementation Strategy

Phase 1: Update Detection and Compatibility (Weeks 1-4)

Implement automated update detection for OS, software, and collections
Create initial compatibility matrix for current supported platforms
Build compatibility validation framework
Establish update notification system

Phase 2: Automated Testing Infrastructure (Weeks 5-8)

Create isolated test environments for each OS/software combination
Implement automated test suites for update validation
Build integration testing across plugin ecosystem
Establish performance and security validation

Phase 3: AI Integration and Guidance (Weeks 9-12)

Integrate update detection with AI assistant
Implement AI-powered impact analysis and recommendations
Create interactive update planning and guidance
Build learning system from update outcomes

Phase 4: Production Pipeline (Weeks 13-16)

Implement staged rollout pipeline with approval gates
Create automated rollback mechanisms
Establish monitoring and alerting for update issues
Build reporting and analytics for update success rates

Update Scenarios and Responses

New Operating System Release (e.g., RHEL 11)

Scenario: RHEL 11 Beta Released
Response:
Detection: Automated scan identifies RHEL 11 availability
Analysis: AI assistant analyzes breaking changes and compatibility
Planning: Create RHEL 11 plugin development roadmap
Testing: Establish RHEL 11 test environment
Development: Update/create RHEL 11 plugin
Validation: Comprehensive testing across plugin ecosystem
Documentation: Update compatibility matrix and user guides
Release: Staged rollout with user notification

Critical Security Update (e.g., Ansible CVE)

Scenario: Critical Ansible Core Vulnerability
Response:
Detection: Security scanner identifies CVE
Impact: AI analysis of affected deployments
Priority: Automatic escalation for critical vulnerabilities
Testing: Expedited testing in staging environments
Deployment: Emergency update pipeline with accelerated approval
Notification: Immediate user notification with guidance
Monitoring: Enhanced monitoring for update-related issues

Collection Update (e.g., New Galaxy Release)

Scenario: qubinode_kvmhost_setup_collection v3.0.0
Response:
Detection: Galaxy API monitoring identifies new version
Compatibility: Check against current OS support matrix
Testing: Automated testing with existing Navigator versions
Integration: Update requirements.yml and test integration
Documentation: Update deployment guides and examples
Release: Coordinated release with collection update

Automation Framework

Update Detection Service

class UpdateService:
    def __init__(self):
        self.detectors = [
            OSUpdateDetector(),
            SoftwareUpdateDetector(), 
            CollectionUpdateDetector(),
            SecurityUpdateDetector(),
            PluginUpdateDetector()
        ]
        
    def scan_for_updates(self) -> UpdateReport:
        """Comprehensive update scanning"""
        updates = []
        for detector in self.detectors:
            updates.extend(detector.detect())
            
        return UpdateReport(
            updates=updates,
            compatibility_analysis=self.analyze_compatibility(updates),
            recommendations=self.ai_assistant.generate_recommendations(updates)
        )

Compatibility Validation

class CompatibilityValidator:
    def validate_update(self, update: Update) -> ValidationResult:
        """Validate update against compatibility matrix"""
        matrix = self.load_compatibility_matrix()
        
        conflicts = self.check_conflicts(update, matrix)
        dependencies = self.check_dependencies(update, matrix)
        breaking_changes = self.check_breaking_changes(update, matrix)
        
        return ValidationResult(
            compatible=len(conflicts) == 0,
            conflicts=conflicts,
            dependencies=dependencies,
            breaking_changes=breaking_changes,
            recommendations=self.generate_recommendations(update)
        )

Success Metrics

Update Velocity

Detection Time: Time from release to detection (target: <24 hours)
Validation Time: Time from detection to validation completion (target: <72 hours)
Deployment Time: Time from validation to production deployment (target: <1 week)

Quality Metrics

Update Success Rate: Percentage of updates deployed without rollback (target: >95%)
Compatibility Accuracy: Accuracy of compatibility predictions (target: >98%)
Security Response Time: Time to deploy critical security updates (target: <48 hours)

User Experience

Update Notification Relevance: User satisfaction with update notifications
Guidance Effectiveness: Success rate of AI-guided updates
Documentation Currency: Percentage of documentation updated within 1 week of software changes

ADR-0026: RHEL 10/CentOS 10 Platform Support Strategy (OS update example)
ADR-0027: CPU-Based AI Deployment Assistant Architecture (AI guidance integration)
ADR-0028: Modular Plugin Framework for Extensibility (plugin update management)
ADR-0029: Documentation Strategy and Website Modernization (documentation sync)

Date

2025-11-07

Stakeholders

Platform Engineering Team
DevOps Team
Security Team
QA Team
AI/ML Team
Community Contributors